{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# MLP训练与评估",
   "id": "abc39beb06f3e571"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 数据准备与预处理",
   "id": "8eeb3abe259c448c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:13:28.642456Z",
     "start_time": "2025-06-12T13:13:28.574646Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 所有导入放在这里\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import time  # <-- 这里导入\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ],
   "id": "102a442127672e7f",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:13:52.693079Z",
     "start_time": "2025-06-12T13:13:28.650440Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "# 加载MNIST数据集\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ],
   "id": "947d3b6dd20be7ef",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\sklearn\\datasets\\_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:13:53.577020Z",
     "start_time": "2025-06-12T13:13:52.756965Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "501f93cf5437140f",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:13:54.506147Z",
     "start_time": "2025-06-12T13:13:53.641594Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ],
   "id": "3dd89098157433ad",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## MLP 模型训练与评估",
   "id": "24484750db8ec024"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:13:54.584059Z",
     "start_time": "2025-06-12T13:13:54.569035Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import timeit\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ],
   "id": "d6685c5dfa8d2a1",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:13:54.646802Z",
     "start_time": "2025-06-12T13:13:54.632696Z"
    }
   },
   "cell_type": "code",
   "source": [
    "### MLP模型训练\n",
    "# 创建MLP分类器(包含2个隐藏层：512和256个神经元)\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(512, 256),\n",
    "                    activation='relu',\n",
    "                    solver='adam',\n",
    "                    max_iter=1000,\n",
    "                    random_state=42)"
   ],
   "id": "7aecc58e4628289d",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:18:20.581895Z",
     "start_time": "2025-06-12T13:13:54.754697Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 单次训练计时\n",
    "print(\"\\n开始训练...\")\n",
    "start_time = time.time()\n",
    "mlp.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "print(f\"训练完成，耗时: {train_time:.4f} 秒\")"
   ],
   "id": "6bb42482572c3f65",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "开始训练...\n",
      "训练完成，耗时: 265.8096 秒\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:18:20.887649Z",
     "start_time": "2025-06-12T13:18:20.769005Z"
    }
   },
   "cell_type": "code",
   "source": [
    "### 模型预测\n",
    "y_pred_mlp = mlp.predict(X_test)"
   ],
   "id": "6ece7e3047202734",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:18:20.968257Z",
     "start_time": "2025-06-12T13:18:20.890078Z"
    }
   },
   "cell_type": "code",
   "source": [
    "### 模型评估\n",
    "accuracy_mlp = accuracy_score(y_test, y_pred_mlp)\n",
    "precision_mlp, recall_mlp, f1_mlp, _ = precision_recall_fscore_support(y_test, y_pred_mlp, average='weighted')\n",
    "conf_matrix_mlp = confusion_matrix(y_test, y_pred_mlp)\n",
    "class_report_mlp = classification_report(y_test, y_pred_mlp)"
   ],
   "id": "ce2af76a936e549",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:18:21.030747Z",
     "start_time": "2025-06-12T13:18:21.015093Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"\\nMLP性能指标:\")\n",
    "print(f\"Accuracy: {accuracy_mlp:.4f}\")\n",
    "print(f\"Precision: {precision_mlp:.4f}\")\n",
    "print(f\"Recall: {recall_mlp:.4f}\")\n",
    "print(f\"F1-score: {f1_mlp:.4f}\")\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix_mlp)\n",
    "print(\"Classification Report:\\n\", class_report_mlp)"
   ],
   "id": "54bcd861b993963a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MLP性能指标:\n",
      "Accuracy: 0.9783\n",
      "Precision: 0.9783\n",
      "Recall: 0.9783\n",
      "F1-score: 0.9783\n",
      "Confusion Matrix:\n",
      " [[1332    0    1    0    0    0    7    0    3    0]\n",
      " [   0 1589    2    1    1    0    0    6    0    1]\n",
      " [   0    3 1356    2    3    1    1    4    8    2]\n",
      " [   0    1   12 1389    2   14    0    5    7    3]\n",
      " [   0    0    3    0 1271    0    3    3    2   13]\n",
      " [   1    3    1   10    1 1235   13    0    8    1]\n",
      " [   2    1    3    0    6    4 1373    1    5    1]\n",
      " [   1    2   14    1    4    1    0 1463    2   15]\n",
      " [   1    7    6    6    1    7    3    3 1318    5]\n",
      " [   4    4    2    4   10    1    0   11   14 1370]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1343\n",
      "           1       0.99      0.99      0.99      1600\n",
      "           2       0.97      0.98      0.98      1380\n",
      "           3       0.98      0.97      0.98      1433\n",
      "           4       0.98      0.98      0.98      1295\n",
      "           5       0.98      0.97      0.97      1273\n",
      "           6       0.98      0.98      0.98      1396\n",
      "           7       0.98      0.97      0.98      1503\n",
      "           8       0.96      0.97      0.97      1357\n",
      "           9       0.97      0.96      0.97      1420\n",
      "\n",
      "    accuracy                           0.98     14000\n",
      "   macro avg       0.98      0.98      0.98     14000\n",
      "weighted avg       0.98      0.98      0.98     14000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:18:21.525895Z",
     "start_time": "2025-06-12T13:18:21.081184Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 混淆矩阵热度图\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(conf_matrix_mlp, annot=True, fmt='d', cmap='Reds')\n",
    "plt.title(\"MLP - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "id": "203ced50a17eb8e9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:18:21.755616Z",
     "start_time": "2025-06-12T13:18:21.590214Z"
    }
   },
   "cell_type": "code",
   "source": [
    "metrics_df = pd.DataFrame({\n",
    "    'Model': ['MLP'],\n",
    "    'Accuracy': [0.9740],\n",
    "    'Precision': [0.9741],\n",
    "    'Recall': [0.9740],\n",
    "    'F1': [0.9740]\n",
    "}).set_index('Model')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.heatmap(metrics_df, \n",
    "                annot=True, \n",
    "                fmt=\".2%\",\n",
    "                cmap=\"YlGnBu\", \n",
    "                linewidths=.5,\n",
    "                cbar_kws={'label': 'Score Scale'},\n",
    "                annot_kws={\"size\": 12})\n",
    "\n",
    "plt.title(\"Model Performance Comparison\", fontsize=14, pad=20)\n",
    "plt.xticks(rotation=45, ha='right', fontsize=12)\n",
    "plt.yticks(rotation=0, fontsize=12)\n",
    "ax.xaxis.set_label_position('top') \n",
    "ax.xaxis.tick_top()\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Score (0-1 scale)', rotation=270, labelpad=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "54e6f6dfbe37745c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
